AI-Driven Intelligence in Last-Mile Logistics: Integrating Autonomous Fleet Management, Predictive Maintenance, and Urban Delivery Ecosystems

Abstract

The rapid acceleration of urbanization, the exponential growth of e-commerce, and increasing consumer expectations for speed, transparency, and reliability have collectively transformed last-mile logistics into one of the most complex and strategically critical segments of contemporary supply chains. Within this evolving landscape, artificial intelligence has emerged not merely as a supportive technological tool but as a foundational paradigm reshaping how fleets are managed, how vehicles are maintained, and how autonomous systems interact with dynamic urban environments. This research article undertakes a comprehensive and theoretically grounded examination of AI-driven fleet management and predictive maintenance within the broader context of last-mile delivery systems, with a particular emphasis on autonomous and semi-autonomous vehicles operating in dense metropolitan settings.

The study situates AI-enhanced fleet intelligence at the intersection of data science, machine learning, cyber-physical systems, and logistics theory, arguing that predictive maintenance and real-time decision-making are no longer discrete operational functions but integral components of an interconnected, adaptive logistics ecosystem. Drawing extensively on recent scholarly contributions, including foundational work on AI-enhanced fleet management and predictive maintenance for autonomous vehicles (Patil & Deshpande, 2025), the article explores how algorithmic foresight enables a shift from reactive maintenance models toward anticipatory, condition-based strategies that significantly alter cost structures, safety outcomes, and asset lifecycles. In parallel, the research contextualizes these developments within the expanding global last-mile delivery market, where urban congestion, sustainability pressures, and labor constraints intensify the need for intelligent automation (Statista Inc., 2023; Boysen et al., 2021).

Methodologically, the article adopts an integrative qualitative research design grounded in systematic literature synthesis, comparative conceptual analysis, and interpretive evaluation of industry reports, policy frameworks, and academic models. Rather than relying on empirical datasets or mathematical formalism, the study emphasizes deep theoretical elaboration and critical discourse, aligning with calls in logistics and systems engineering scholarship for more conceptually robust examinations of AI adoption (Masorgo et al., 2024; Giuffrida et al., 2022). The results section synthesizes emergent patterns across the literature, demonstrating how AI-enabled telemetry, real-time route optimization, and predictive diagnostics collectively redefine operational visibility and strategic control in fleet-based logistics (Drozdov, 2024; Microsoft Azure, 2024).

The discussion extends beyond technical efficiency to interrogate broader implications, including human–machine collaboration in Industry 5.0 contexts, ethical governance of autonomous decision-making, and the socio-technical resilience of urban delivery infrastructures (Tóth et al., 2023; Yahya et al., 2021). By critically engaging with counter-arguments concerning data dependency, algorithmic bias, and infrastructural inequality, the article contributes a nuanced perspective on both the promises and limitations of AI-driven fleet intelligence. Ultimately, this research positions AI-enhanced fleet management and predictive maintenance not as incremental innovations but as transformative forces capable of reshaping the future of last-mile logistics in increasingly complex urban ecosystems.